Name
Title | ||
|---|---|---|
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection |
Abstract | ||
|---|---|---|
Association rules are a data mining technique used to discover frequent patterns in a data set. In this work, association rules are used in the medical domain, where data sets are generally high dimensional and small. The chief disadvantage about mining ... |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/s10115-005-0212-y | Knowl. Inf. Syst. |
Keywords | DocType | Volume |
high dimensional,class noise,noisy data stream,stream data mining,attribute-oriented dynamic classifier selection,medical domain,association rule,effective classification,data mining technique,classifler ensemble,classiflcation,multiple classifler systems,chief disadvantage,dynamic classifler selection,frequent pattern,comparative study,sensor network,data mining,classification | Journal | 9 |
Issue | ISSN | Citations |
3 | 0219-3116 | 22 |
PageRank | References | Authors |
0.99 | 31 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingquan Zhu | 1 | 3086 | 181.95 |
| Xindong Wu | 2 | 8830 | 503.63 |
| Ying Yang | 3 | 206 | 10.51 |